As can be seen from the figure, STEP outperforms STEP w/o GSL, which shows that our graph structure learning module consistently plays a positive role.
Meanwhile, STEP w/o GSL still achieves satisfactory performance, demonstrating that segment-level representation plays a vital role.
STEP also outperforms STEP w/o reg, showing that the long sequence representations of TSFormer is superior in improving the graph quality.
In addition, as mentioned in Section , DCRNN represents a large class of STGNNs [1]}, [2]}, [3]}, [4]} that are based on the seq2seq [5]} architecture.
We fuse the representation of TSFormer to the latent representations of the seq2seq encoder according to Eq.(REF ).
We can see that STEP significantly enhances the performance of DCRNN, which verifies the generality of STEP.
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